Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | SIGSPATIAL PhD '14 |
| Untertitel | Proceedings of the 1st ACM SIGSPATIAL PhD Workshop |
| Herausgeber/-innen | Ugur Demiryurek, Mohamed Sarwat |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9781450331586 |
| Publikationsstatus | Veröffentlicht - 4 Nov. 2014 |
| Veranstaltung | 2014 1st ACM SIGPATIAL PhD Workshop, SIGSPATIAL PhD 2014 - In Conjunction with 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2014 - Dallas, USA / Vereinigte Staaten Dauer: 4 Nov. 2014 → 7 Nov. 2014 |
Abstract
Spatial data integration is a challenging task due to the high degree of diversity between different geodata sources, the inherent complexity of objects, and the large size of datasets. To avoid duplicates in an integrated dataset, input sources have to be linked on the instance level. By matching spatial objects, multiple representations of the same real-world entity shall be identified based on similarity computation. In this paper, we present an approach for similarity-based spatial matching of road networks. Our SimMatching algorithm adapts to a variety of input data characteristics by using weighted similarity measures. Geometric and semantic attributes are considered as well as the dataset topology to enhance similarity computations with relational measures. We use a greedy approach and optimizations to keep the number of match candidates minimal all the time. This allows very low runtimes while giving high quality matching results. Supported by a partitioning framework and parallel processing, it also guarantees scalability to large datasets.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
- RIS
SIGSPATIAL PhD '14: Proceedings of the 1st ACM SIGSPATIAL PhD Workshop. Hrsg. / Ugur Demiryurek; Mohamed Sarwat. 2014. 2694866.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - SimMatching
T2 - 2014 1st ACM SIGPATIAL PhD Workshop, SIGSPATIAL PhD 2014 - In Conjunction with 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2014
AU - Schäfers, Michael
AU - Lipeck, Udo W.
PY - 2014/11/4
Y1 - 2014/11/4
N2 - Spatial data integration is a challenging task due to the high degree of diversity between different geodata sources, the inherent complexity of objects, and the large size of datasets. To avoid duplicates in an integrated dataset, input sources have to be linked on the instance level. By matching spatial objects, multiple representations of the same real-world entity shall be identified based on similarity computation. In this paper, we present an approach for similarity-based spatial matching of road networks. Our SimMatching algorithm adapts to a variety of input data characteristics by using weighted similarity measures. Geometric and semantic attributes are considered as well as the dataset topology to enhance similarity computations with relational measures. We use a greedy approach and optimizations to keep the number of match candidates minimal all the time. This allows very low runtimes while giving high quality matching results. Supported by a partitioning framework and parallel processing, it also guarantees scalability to large datasets.
AB - Spatial data integration is a challenging task due to the high degree of diversity between different geodata sources, the inherent complexity of objects, and the large size of datasets. To avoid duplicates in an integrated dataset, input sources have to be linked on the instance level. By matching spatial objects, multiple representations of the same real-world entity shall be identified based on similarity computation. In this paper, we present an approach for similarity-based spatial matching of road networks. Our SimMatching algorithm adapts to a variety of input data characteristics by using weighted similarity measures. Geometric and semantic attributes are considered as well as the dataset topology to enhance similarity computations with relational measures. We use a greedy approach and optimizations to keep the number of match candidates minimal all the time. This allows very low runtimes while giving high quality matching results. Supported by a partitioning framework and parallel processing, it also guarantees scalability to large datasets.
KW - Data Matching
KW - Road Networks
KW - Scalability
KW - Similarity
KW - Spatial Data Integration
KW - Spatial Databases
UR - http://www.scopus.com/inward/record.url?scp=84928024782&partnerID=8YFLogxK
U2 - 10.1145/2694859.2694866
DO - 10.1145/2694859.2694866
M3 - Conference contribution
AN - SCOPUS:84928024782
BT - SIGSPATIAL PhD '14
A2 - Demiryurek, Ugur
A2 - Sarwat, Mohamed
Y2 - 4 November 2014 through 7 November 2014
ER -